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Several central banks have leaned against the wind in the housing market by increasing the policy rate preemptively to prevent a bubble. Yet the empirical literature provides mixed results on the impact of short-term interest rates on house prices: the estimated semi-elasticities range from -12 to positive values. To assign a pattern to these differences, we collect 1,447 estimates from 31 individual studies that cover 45 countries and 69 years. We then relate the estimates to 39 characteristics of the financial system, business cycle, and estimation approach. Our main results are threefold. First, the mean reported estimate is exaggerated by publication bias, because insignificant results are underreported. Second, omission of important variables (liquidity and long-term rates) likewise exaggerates the effects of short-term rates on house prices. Third, the effects are stronger in countries with more developed mortgage markets and generally later in the cycle when the yield curve is flat and house prices enter an upward spiral.

The importance of replication in the social and behavioural sciences has been emphasized for decades. Various frequentist and Bayesian approaches have been proposed to qualify a replication study as successful or unsuccessful. One of them is meta-analysis. The focus of the present study is on the way meta-analysis functions as a replication success metric. To investigate this, original and replication studies that are part of two large-scale replication projects were used. For each original study, the probability of replication success was calculated using meta-analysis under different assumptions of the underlying population effect when replication results were unknown. The accuracy of the predicted overall replication success was evaluated once replication results became available using adjusted Brier scores. Our results showed that meta-analysis performed poorly when used as a replication success metric. In many cases, quantifying replication success using meta-analysis resulted in the conclusion where the replication was deemed a success regardless of the results of the replication study.

We conduct a meta-analysis of 1,973 estimates of stock price responses to shareholder activism reported in 67 primary studies. We document publication bias in the literature. Corrected activism effects range from 0% to 1.5%. Effects are stronger when shareholder rights are better protected and when stock markets are smaller. Markets respond more positively to activism by individual investors, confrontational activism, and activism aimed at company sale. Estimates based on longer periods, simpler risk-adjustment approaches, more recent and longer datasets, as well as those published in more reputable journals tend to be larger.

We plan a collaborative research analysis of the original ‘many analysts’ study by Silberzahn et al.1 testing whether European soccer referees were skin-tone biased in giving red cards. Their study concluded that the answer has great model dependency, and in this sense is unclear. Auspurg and Brüderl2 re-analysed the methods and data and argued the answer is clearer when attending to causal inference and outliers. Researchers involved in the original study and follow-up agreed to an adversarial collaboration to better answer this pertinent research question and sources of variability in answers. They recruited other scholars to provide independent methodological and theoretical insights. The result is this study where together we jointly tackle the skin-tone bias hypothesis and the ‘parsing problem’ of identifying meaningful moderators that explain effect size dispersion across a multiverse of alternative specifications. We build on Auspurg and Brüderl’s proposal that analysts in the original study had very different interpretations of the conceptual research question, leading them to different empirical research questions, which in turn produced results that are not directly comparable. This pre-analysis plan describes our motivation, methods, and plan to contextualize our findings in larger discussions of race bias, multiverse analysis parsing, and subjectivity in science.

Class size reduction mandates are frequent and invariably justified by studies reporting positive effects on student achievement. Yet other studies report no effects, and the literature as a whole awaits correction for potential publication bias. Moreover, if identification drives results systematically, the relevance of individual studies will vary. We build a sample of 1,767 estimates collected from 62 studies and for each estimate codify 42 factors reflecting estimation context. We employ recently developed nonlinear techniques for publication bias correction and Bayesian model averaging techniques that address model uncertainty. The results suggest publication bias among studies featured in top five economics journals, but not elsewhere. The implied class size effect is zero for all identification approaches except Tennessees Student/Teacher Achievement Ratio project. The effect remains zero for disadvantaged students and across subjects, school types, and countries.

Publishing study results in scientific journals has been the standard way of disseminating science. However, getting results published may depend on their statistical significance. The consequence of this is that the representation of scientific knowledge might be biased. This type of bias has been called publication bias. In the present study, we make an attempt to get more insight into publication bias by examining it at the author, reviewer, and editor level. Additionally, we make a direct comparison between publication bias induced by authors, by reviewers, and by editors. Our findings suggest that statistically significant findings have a higher likelihood to be published than statistically non-significant findings, because (1) authors are more likely to write up and submit articles with significant results compared to articles with non-significant results; (2) reviewers give more favourable reviews to articles with significant results compared to articles with non-significant results; and (3) editors are more likely to accept for publication articles with significant results compared to articles with non-significant results. Evidence on differences in the relative contributions to publication bias by authors, reviewers, and editors is ambiguous.

Meta-analysis upweights studies reporting lower standard errors and hence more precision. But in empirical practice, notably in observational research, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations show that a modest dose of spurious precision creates a formidable problem for inverse-variance weighting and bias-correction methods based on the funnel plot. Selection models fail to solve the problem, and the simple mean can beat sophisticated estimators. Cures to publication bias may become worse than the disease. We introduce an approach that surmounts spuriousness: the Meta-Analysis Instrumental Variable Estimator (MAIVE), which employs inverse sample size as an instrument for reported variance.

Standard economics models require that financial incentives improve performance, while leading theories in psychology allow for the opposite. Experimental results are mixed, and so far have not been corrected for publication bias and model uncertainty. We collect 1,568 economics estimates together with 46 factors capturing the context in which the estimates were obtained. We use novel nonlinear techniques to correct for publication bias and employ Bayesian model averaging to account for model uncertainty. The corrected estimates are zero or tiny across contexts of field experiments, including differences in performance measurement, task definition, reward size and framing, motivation beyond money, subject pool, and estimation technique. Laboratory experiments produce statistically significant estimates on average after correction for publication bias, but even there the effect is weak. Experimental economics evidence is inconsistent with standard economics models.

The article analyzes the realpolitik strategic offensive behavior of PRC’s centralized banking after joining the GATT and WTO. With the Bretton Woods and issuance of USD anchored to gold, the PRC State-owned Assets Supervision and Administration (SASAC) of the import-export control regime enabled the “double-circulation” operations of the RMB and offshore RMB, with quantitative restriction distributed to the commercial banking in private dealership in currency exchanges and banning on bona fide private dealership. The gold and oil hedging on the free market further enabled the Chinese central bank’s “monetary conduction mechanism” in issuing money according to USD for strategic offense with the currency price market. With Brexit and Trade War, PRC currently has blocked almost all international settlement channels in the controlled market in maintaining offshore RMB value evaluations. With the background of cryptocurrency and PRC’s approach in digitization of economic control, the article analyzes possible resolutions for economic justice.

Bank Syariah adalah lembaga keuangan yang usaha pokoknya memberi layanan pembiayaan kredit dan jasa dalam lau lintas pembayaran serta peredaran uang yang beroperasi disesuaikan dengan prinsip- prinsip syariah.Lembaga keuangan non bank adalah semua badan yang melakukan kegiatan di bidang keuangan, yang secara langsung atau tidak langsung menghimpun dana terutama dengan jalan mengeluarkan kertas berharga dan menyalurkan dalam masyarakat terutama guna membiayai investasi perusahaan untuk mendapatkan kemakmuran dan keadilan masyarakat.

This study presents a comparative analysis of innovative development, advantages and threats of the Arctic zones belonging to Russia, Norway and Sweden by rating positioning using mathematical analysis methods.Purpose and Methods: In order to determine the strengths and weaknesses of Russia among its Arctic competitors (the neighboring countries Norway and Sweden), a comparative analysis was carried out using the index (coefficient) of innovative development of all these research subjects. The study also included the comparison of the parameters that make up its categories (enlarged group indicators) and subcategories (single indicators included in the categories). The analysis used data from the. The results of the comparative analysis showed that for most of the studied indicators included in the subcategories ('labor productivity', 'foundation of new enterprises', 'certification', 'high-tech production', etc.), Russia lags far behind Norway and Sweden. Russia's advantages lie in such subcategories as the development of innovative links between universities and the industrial sector in R&D, trade, diversification, and scope of market. Russia also successfully competes in such subcategories as business environment formation and creation of information and communication technologies. It was noted that these areas are promising for Russia and they should be given great attention to increase the competitiveness of Russia's Arctic. In conclusion, it is supposed that Russia has significant triggers for its competitiveness in the near future along with the significant lag from the analyzed Arctic neighbors in terms of the integral value of the innovation development index and its place in the world ranking.

A considerable proportion of psychological research has not been replicable, and estimates range from 9% to 77% for nonreplicable results. The extent to which vast proportions of studies in the field are replicable is still unknown, as researchers lack incentives for publishing individual replication studies. When preregistering replication studies via the Open Science Foundation website (OSF, osf.io), researchers can publicly register their results without having to publish them and thus circumvent file-drawer effects. We analyzed data from 139 replication studies for which the results were publicly registered on the OSF and found that out of 62 reports that included the authors’ assessments, 23 were categorized as “informative failures to replicate” by the original authors. 24 studies allowed for comparisons between the original and replication effect sizes, and whereas 75% of the original effects were statistically significant, only 30% of the replication effects were. The replication effects were also significantly smaller than the original effects (approx. 38% the size). Replication closeness did not moderate the difference between the original and the replication effects. Our results provide a glimpse into estimating replicability for studies from a wide range of psychological fields chosen for replication by independent groups of researchers. We invite researchers to browse the Replication Database (ReD) ShinyApp, which we created to check for whether seminal studies from their respective fields have been replicated. Our data and code are available online: https://osf.io/9r62x/

Amazon Mechanical Turk is a very widely-used tool in business and economics research, but how trustworthy are results from well-published studies that use it? Analyzing the universe of hypotheses tested on the platform and published in leading journals between 2010 and 2020 we find evidence of widespread p-hacking, publication bias and over-reliance on results from plausibly under-powered studies. Even ignoring questions arising from the characteristics and behaviors of study recruits, the conduct of the research community itself erode substantially the credibility of these studies' conclusions. The extent of the problems vary across the business, economics, management and marketing research fields (with marketing especially afflicted). The problems are not getting better over time and are much more prevalent than in a comparison set of non-online experiments. We explore correlates of increased credibility.

Randomized controlled trials (RCTs) are increasingly prominent in economics, with pre-registration and pre-analysis plans (PAPs) promoted as important in ensuring the credibility of findings. We investigate whether these tools reduce the extent of p-hacking and publication bias by collecting and studying the universe of test statistics, 15,992 in total, from RCTs published in 15 leading economics journals from 2018 through 2021. In our primary analysis, we find no meaningful difference in the distribution of test statistics from pre-registered studies, compared to their non-pre-registered counterparts. However, pre-registered studies that have a complete PAP are significantly less p-hacked. These results point to the importance of PAPs, rather than pre-registration in itself, in ensuring credibility.

Data-driven policy-making is the most effective way to alleviate poverty and improve human welfare. However, these policies are only as good as the data they are based on, and it appears that much of this data is systematically biased against women. This poses a crucial problem not just for policy design, but for the practice of science itself. Unfortunately, resolving this gendered research gap has remained a low priority in science, and many researchers treat it as an extraneous nuisance to their work. But women represent half of the world, so any effective and accurate scientific contribution must prioritize understanding and incorporating their experiences into the research process. Otherwise, we risk reinforcing a “world designed for men.” This research agenda outlines questions and topics to help us improve our understanding of gender bias in the research process.

Promoting the economic empowerment of women requires more than just helping them get a foot in the door and hoping for the best. We need women to remain in the workplace, to thrive in their work environments, job descriptions and careers, and to derive the self-efficacy and emotional fulfillment that comes from work. To achieve this, we need to recognize that few workplaces are gender neutral. Instead, they harbor norms and protocols that accommodate male preferences and emotional needs, while neglecting those of women. This bias will push women out or undermine their success. Unfortunately, we feel that the international development agenda has not paid sufficient attention to the importance of female workplace retention or the emotional toil that mismatched norms can inflict. This research agenda hopes to address this failing by outlining ways we can identify, understand, and correct norms and patterns that make it difficult for women to continue working. We feel that this is particularly important in many workplaces of the Global South, where norms prevail over formal procedures, and it will become more relevant as businesses in LICs formalize and establish organizational rules.

We provide the first quantitative survey of the empirical literature on hedge fund performance. We examine the impact of potential biases on the reported results. Empirical analysis in prior studies has been plagued by fragmentation of underlying data and by limited consensus on how hedge fund performance should be measured. Using a sample of 1,019 intercept terms from regressions of hedge fund returns on risk factors (the “alphas”) collected from 74 studies published between 2001 and 2021 we show that inferences about hedge fund returns are not significantly contaminated by publication selection bias. Most of our monthly alpha estimates adjusted for the (small) bias fall within a relatively narrow range of 30 to 40 basis points. Considering several partitions of our sample, we document a modest publication bias only for estimates based on instrumental variables (IV), for which relatively large standard errors are common and that tend to be less precise. In contrast, studies that explicitly control for the potential biases in the underlying data (e.g. the backfilling bias and the survivorship bias) report lower alphas. Our results demonstrate that despite the prevalence of the publication selection bias in numerous other research settings, publication may not be selective when there is no strong a priori theoretical prediction about the sign of estimated coefficients, which may induce greater readiness to publish statistically insignificant results.

We collect 1,021 estimates from 92 studies that use the consumption Euler equation to measure relative risk aversion and that disentangle it from intertemporal substitution. We show that calibrations of risk aversion are typically larger than estimates thereof. Moreover, reported estimates are typically larger than the underlying risk aversion because of publication bias. After correction for the bias, the literature suggests a mean risk aversion of 1 in economics and 2--7 in finance contexts. The reported estimates are systematically driven by the characteristics of data (frequency, dimension, country, stockholding) and utility (functional form, treatment of durables). To obtain these results we use nonlinear techniques to correct for publication bias and Bayesian model averaging techniques to account for model uncertainty.

We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.Wagenmakers and colleagues describe seven statistical procedures that increase transparency in data analysis. These procedures highlight common ground among data analysts from different schools and find inspiration in Merton's ethos of science.

The evidence-based policy movement promotes the use of empirical evidence to inform policy decision-making. While several social science disciplines are undergoing a ‘credibility revolution’ focused on openness and replication, policy analysis has yet to systematically embrace transparency and reproducibility. We argue that policy analysis should adopt the open research practices increasingly espoused in related disciplines to advance the credibility of evidence-based policy making. We first discuss the importance of evidence-based policy in an era of increasing disagreement about facts, analysis, and expertise. We present a novel framework for ‘open’ policy analysis (OPA) and how to achieve it, focusing on examples of recent policy analyses that have incorporated open research practices such as transparent reporting, open data, and code sharing. We conclude with recommendations on how key stakeholders in evidence-based policy can make OPA the norm and thus safeguard trust in using empirical evidence to inform important public policy decisions.

Koperasi adalah badan usaha yg melakukan aktivitas menurut asas koperasi sekaligus sebagai penggerak ekonomi kerakyatan yg menurut asas kekeluargaan. Selain itu, koperasi sebagai gerakan ekonomi kerakyatan yg berorientasi pada pembinaan partisipasi masyarakat dalam rangka memperkuatstruktur perekonomian nasional dengan demokrasi ekonomi menurut asas kekeluargaan

Using a sample of 70,399 published p-values from 192 meta-analyses, we empirically estimate the counterfactual distribution of p-values in the absence of any biases. Comparing observed p-values with counterfactually expected p-values allows us to estimate how many p-values are published as being statistically significant when they should have been published as being non-significant. We estimate the extent of inflated significance to range between 56.2% and 71.3% of the significant p-values. Subsample analysis suggests that the extent of inflated significance is reduced in research fields that use experimental designs, analyze microeconomics research questions and have at least some adequately powered studies.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

In this article, we present statacons, an SCons-based build tool for Stata. Because of the integration of Stata and Python in recent versions of Stata, we are able to adapt SCons for Stata workflows without the use of an external shell or extensive configuration. We discuss the usefulness of build tools generally, provide examples of the use of statacons in Stata workflows, present key elements of the syntax of statacons, and discuss extensions, alternatives, and limitations. We provide recommendations for collaborative workflows and, at the end of the article, installation instructions.

Scientists' ability to integrate diverse forms of evidence and evaluate how well they can explain and predict phenomena, in other words, $\textit{to know how much they know}$, struggles to keep pace with technological innovation. Central to the challenge of extracting knowledge from data is the need to develop a metric of knowledge itself. A candidate metric of knowledge, $K$, was recently proposed by the author. This essay further advances and integrates that proposal, by developing a methodology to measure its key variable, symbolized with the Greek letter $\tau$ ("tau"). It will be shown how a $\tau$ can represent the description of any phenomenon, any theory to explain it, and any methodology to study it, allowing the knowledge about that phenomenon to be measured with $K$.To illustrate potential applications, the essay calculates $\tau$ and $K$ values of: logical syllogisms and proofs, mathematical calculations, empirical quantitative knowledge, statistical model selection problems, including how to correct for "forking paths" and "P-hacking" biases, randomised controlled experiments, reproducibility and replicability, qualitative analyses via process tracing, and mixed quantitative and qualitative evidence.Whilst preliminary in many respects, these results suggest that $K$ theory offers a meaningful understanding of knowledge, which makes testable metascientific predictions, and which may be used to analyse and integrate qualitative and quantitative evidence to tackle complex problems.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings. ispartof: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA vol:119 issue:44 ispartof: location:United States status: published

Educational outcomes have many determinants, but one that most young people can readily control is choosing whether to work while in school. Sixty-nine studies have estimated the effect, but results vary from large negative to positive estimates. We show that the results are systematically driven by context, publication bias, and treatment of endogeneity. Studies ignoring endogeneity suffer from an upward bias, which is almost fully compensated by publication selection in favor of negative estimates. Net of the biases, the literature suggests a negative but economically inconsequential mean effect. The effect is more negative for high-intensity employment and educational outcomes measured as decisions to dropout, but it is positive in Germany. To derive these results we collect 861 previously reported estimates together with 32 variables reflecting estimation context, use recently developed nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to assign a pattern to the heterogeneity in the literature.

Measures like pre-analysis plans ask researchers to describe planned data collection and justify data exclusions, but they provide little enforceable oversight of primary data collection. We show that a simple algorithm can select large subsets of data that yield economically meaningful and statistically significant treatment effects. The subsets cannot be distinguished from a random sample of the original data, rendering the selection undetectable if peer reviewers are unaware of the size of the original dataset. Our results hold using simulated data and replication data from a well-known study. We show that there are few natural deterrents to dataset manipulation: the results in our selected subset are robust to a range of alternative specifications, our algorithm performs well under complex sampling strategies, and our subset can yield artificially high effects on multiple outcomes. We conclude by proposing a measure to prevent such manipulation in field experiments.

Researchers seeking to replicate original research often need to decide which of several relevant candidates to select for replication. Several strategies for study selection have been proposed, utilizing a variety of observed indicators as criteria for selection. However, few strategies clearly specify the goal of study selection and how that goal is related to the indicators that are utilized. We have previously formalized a decision model of replication study selection in which the goal of study selection is to maximize the expected utility gain of the replication effort. We further define the concept of replication value as a proxy for expected utility gain (Isager et al., 2020). In this article, we propose a quantitative operationalization of replication value. We first discuss how value and uncertainty - the two concepts used to determine replication value – could be estimated via information about citation count and sample size. Second, we propose an equation for combining these indicators into an overall estimate of replication value, which we denote RVCn. Third, we suggest how RVCn could be implemented as part of a broader study selection procedure. Finally, we provide preliminary data suggesting that studies that were in fact selected for replication tend to have relatively high RVCn estimates. The goal of this article is to explain how RVCn is intended to work and, in doing so, demonstrate the many assumptions that should be explicit in any replication study selection strategy.

Clearinghouses are influential repositories of information on the effectiveness of social interventions. To identify which interventions are “evidence-based,” clearinghouses review intervention evaluations using published standards of evidence that focus primarily on internal validity and causal inferences. Open science practices can improve trust in evidence from evaluations on the effectiveness of social interventions. Including open science practices in clearinghouse standards of evidence is one of many efforts that could increase confidence in designations of interventions as “evidence-based.” In this study, we examined the policies, procedures, and practices of 10 federal evidence clearinghouses that review preventive interventions—an important and influential subset of all evidence clearinghouses. We found that seven consider at least one open science practice when evaluating interventions: replication (6 of 10 clearinghouses), public availability of results (6), investigator conflicts of interest (3), design and analysis transparency (3), study registration (2), and protocol sharing (1). We did not identify any policies, procedures, or practices related to analysis plan registration, data sharing, code sharing, material sharing, and citation standards. We provide a framework with specific recommendations to help federal and other evidence clearinghouses implement the Transparency and Openness Promotion (TOP) Guidelines. Our proposed “TOP Guidelines for Clearinghouses” includes reporting whether evaluations used open science practices, incorporating open science practices in their standards for receiving “evidence-based” designations, and verifying that evaluations used open science practices. Doing so could increase the trustworthiness of evidence used for policy making and support improvements throughout the evidence ecosystem.

Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.(JEL C13, C90, I23, J23, J38, L82)

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

BACKGROUND. Experimental vignette research methods have been used to study a diverse range of theoretical and practical issues. Vignettes are designed to create hypothetical cultural/normative contexts for the study of variation in self-reported attitudes. A key problem in such research, however, is potential social desirability response bias. METHOD. A vignette experimental test of an hypothesis derived from a dual-process theory (the MODE framework initially developed by Fazio) linking explicit vs. implicit self-reported attitude measurement and social desirability response bias is reported here. RESULTS. The data show that measuring the social approval of a central vignette character explicitly results in greater social desirability responding than measuring such approval implicitly, supporting MODE theory. CONCLUSIONS. Vignette research methodologies provide a rich, flexible toolkit for studying many important social psychological topics, including issues of inequality and equity. However, researchers can and should design a measurement strategy that carefully manages inferences drawn in light of conditions likely to produce social desirability response bias.

We explore the promise of statistical reform by starting from the assumption that most researchers would endorse Mertons ethos of science as reflected in the four norms of communalism, universalism, disinterestedness, and organized skepticism. Translated to data analysis, these norms imply a need for transparency, a fair acknowledgement of uncertainty, and openness to alternative interpretations. We discuss seven statistical procedures, both old and new, that we believe can positively impact statistical practice in the social and behavioral sciences.

Some previous studies in organizational performance have illustrated that top management team (TMT) characteristics have the ability to lead to more practical strategic decisions, better creativity, value adding innovations and to assist organizations to engage with diverse stakeholders that in effect positively impact on organizational performance. The specific objectives of the study were; to determine the effect of top management team demographic characteristics, to examine the effect of top management team psychological characteristics and to assess the effect of top management team cognitive characteristics on organizational performance of the independent regulatory agencies in Kenya. To achieve these objectives, the study adopted descriptive cross-sectional research design. The target population of the study was all the twenty-three independent regulatory agencies currently existing in Kenya. Due to the uniqueness of each independent regulatory agency and the distinct roles played by each top management team member in their organization, the study adopted a census survey of all the top management team members in all the twenty- three independent regulatory agencies in order to capture the required information. Primary data was gathered using structured questionnaire administered through drop and pick later method. Descriptive statistics was then used to summarize the survey data into percentages, frequencies, means and standard deviations. Inferential statistics employed regression analysis to test hypotheses and draw conclusions. The findings of the study showed that there is no significant effect of top management team demographic characteristics on organizational performance. The findings further showed that top management team psychological characteristics significantly affect organizational performance. Lastly, the findings established that top management team cognitive characteristics significantly affect organizational performance. The study recommends that the recruitment process of TMTs should include psychological and cognitive characteristics as requirements apart from the normal demographic characteristics requirements mostly in use.

Investment is an economic activity that can be a way for a person to expand or maintain his wealth. However, in investing, the public must be more careful in making decisions so that they are not trapped by fake investments. In investing, there are several factors that influence the decision to invest, namely Financial Knowledge, Income, Self-Control, Financial Behavior, and Financial Attitude towards Investment Decisions. This study aims to examine the influence of the variables of Financial Knowledge, Income, and Self-Control on Investing Decisions mediated by Financial Behavior and Financial Attitudes. This study uses Financial Knowledge, Income, and Self-Control as independent variables, then Investment Decisions as the dependent variable, then Financial Behavior, and Financial Attitudes as intervening variables. The results of this study indicate that financial knowledge has an effect on financial behavior. Financial Knowledge affects Financial Attitudes. Financial knowledge influences investment decisions. Income has an effect on Financial Behavior. Income has an effect on Financial Attitudes. Income does not affect the Investment Decision. Self-control affects financial behavior. Self-Control affects Financial Attitudes. Self-Control has no effect on Investment Decisions. Financial Behavior has no effect on Investment Decisions. Financial Attitudes do not affect the Investment Decision.

Profits show the performance of managers before the related parties, such as investors and creditors. Therefore, managers often incorrectly state them in the published annual financial reports to protect their reputation. Undoubtedly, this action needs to be reduced by the governance mechanism, like supervision by institutions as the owner and audit by the external public accountant firm. To control these two effects on profits management, additionally, this study employs financial leverage. This study aims to prove the impact of institutional ownership, the external auditor reputation, and financial leverage on profits management. The population and the samples are the non-financial companies establishing the LQ45 index from 2014 to 2018, getting taken by the simple random sampling technique. Also, the regression model performs as the technique to examine the data. By denoting the testing of the hypothesis results and the discussion section, this study summarizes that institutional ownership and reputable external auditor effectively decrease profits management. Additionally, although firms have much debt, they cut the tendency to manage their profits because of applying transparency.

The tremendous changes that business and economic activities worldwide have gone through in the last few decades, have opened new avenues for IT Freelancing to establish itself as the next big thing in business arena. Bangladesh, transitioning to more IT-centric businesses, holds a huge potential in this domain and has already emerged as a global freelancing hub under government patronage. The main objective of this study is to assess the present status and the future needs of IT Freelancing Business in Bangladesh. This study is of descriptive nature and based on primary and secondary data. Relevant statistical analyses were performed using SPSS. The study found that, 96.2% freelancers are below 35 years old and 80.8% have completed the tertiary education. Significant gender gap exists among the freelancers in terms of participation. It is found that 73.1% freelancers are working on a part-time basis and 33% want to be IT entrepreneur. Half of the freelancers have received training from government and private IT Institutes. Most of the freelancers are satisfied with the earnings from freelancing. Freelancers have observed lack of capital and IT infrastructure support, shortage of training facilities, payment-related issues and social barriers. In order to face the challenges of the fourth industrial revolution head on, the government should take initiatives for ensuring country branding, mobilization of funds, better internet connectivity and steady flow of foreign currency earnings from IT sector and thereby actualizing the ‘Digital Bangladesh’ agenda.

The novel, dreaded, disruptive, and disastrous Covid-19 pandemic took the world by storm in January, 2020. The Covid-19 pandemic in Ghana is part of the worldwide coronavirus disease caused by “severe acute respiratory syndrome (SARS-CoV-2)”. On 12th January, 2020 the World Health Organisation (WHO) confirmed that the novel coronavirus was the cause of a respiratory illness that affected a cluster of people in Wuhan City, Hubei Province, China. This was reported to the WHO on 31st December, 2019. On 11th March, 2020, WHO declared the novel Covid-19 a global pandemic (Graphic Online, 2020a). It is worthy to note how the Government of Ghana, political parties, citizens, scientists and academia, corporate entities, faith based organisations, traditional rulers, have offered varied forms of interventions to combat the scourge. The Theoretical Framework of this research was underpinned by the Theory of Epidemics, the Agency Theory, the Rational Choice Theory, and the Stakeholder Theory. We conducted a cross-sectional research through non-probability and purposive sampling with 250 respondents. We also employed face-to-face interviews, structured closed-ended and open-ended Questionnaires (Braun and Clarke, 2012; Denzin, 2017), which were administered online through email application via Google Forms. One of our major findings was that with the approval of Pfizer/BioNTech Covid-19 vaccine by the UK’s MHRA on 1st December, 2020 (Graphic Online, 2020b); and subsequently by the US FDA a week later on 8th December, 2020 (Graphic Online, 2020c), all governments around the globe in general, but Africa in particular, must make conscious efforts backed by adequate budgetary allocations to secure maximum quantities of the vaccines for their vulnerable teeming population.

The entrepreneurship on mobility leading to an improved quality of life should be an initiative of young people living in the different neighborhoods of a city. They can even design exemplary models of territorial ordering for responsible mobility. Young people with some degree of university education are more sensitive to the value of designing more humane cities. Hence, all social transformation in citizen co-responsibility has its origin in feelings of empathy, that is, in the search for urban happiness for the achievement of a dignified human life. The research question is how to motivate youth social entrepreneurship in city neighborhoods for mobility and strengthening of citizen culture. The research methodology was based on 710 surveys on Quality of Life and Urban Mobility in the Aburra Valley applied to young university students in their last semester and eight interviews with youth organizations and municipal secretariats of citizenship, and mobility. The study conclusion is that if more than 90% of daily visitors to the city downtown live in the surrounding neighborhoods, then the strengthening of civic culture must begin in those neighborhoods. If the suburban area is organized, the downtown will be organized.

To overcome the frequently debated crisis of confidence, replicating studies is becoming increasingly more common. Multiple frequentist and Bayesian measures have been proposed to evaluate whether a replication is successful, but little is known about which method best captures replication success. This study is one of the first attempts to compare a number of quantitative measures of replication success with respect to their ability to draw the correct inference when the underlying truth is known, while taking publication bias into account. Our results show that Bayesian metrics seem to slightly outperform frequentist metrics across the board. Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of extreme publication bias, where this pattern reverses.

Electricity is the key input for urban and rural development which directly effects households’ welfare in micro-level. Least developed countries policy focus on electrifying rural area through off-grid electricity because of high cost in connecting remote areas to national grid. This research estimates the welfare effects of Shorabak small hydropower in Fayzabad city of Badakhshan province, considering the wellbeing of residences in Taliqan city of Takhar provice that obtained from imported electricity from Tajikistan. The dependent variables of education, saving, health, employment, information and environment used as determinant of welfare in linear regression models. Residences of Fayzabad and Taliqan cities constituted the target population, who interviewed through 400 questionnaire using purposive samplings. For the purpose of analysis, regression models run in SPSS version 25. It was found that full access to electricity in Taliqan city positively changed study hours, saving via cheap per kW fee, decreased illness caused by utilizing wood, fuel for cooking and heating purposes. Furthermore, the level of information increased because of access to media particularly TV. A positive notion seen in keeping environment green by removing wood in households as result of using electricity instead. Generally the findings show, by Shorabak hydropower plant inauguration which is 90% completed the same welfare increase will be felt in Fayzabad city as well.

In pursuit of financial intermediation between borrowers and savers banks are exposed to various risks which affect efficiency. Using annual panel data for the period 2010 to 2019, this paper investigates the influence of risk-taking behaviour on bank efficiency in a developing economy. Data envelopment analysis technique was used to obtain the profit efficiency scores of each bank and Tobit regression to estimate the impact of various components of bank risks on profit efficiency. Estimation results established that credit and liquidity risks, significantly influence bank efficiency. Therefore, banks should maintain quality assets and a stable liquidity position as they significantly impact on efficiency.

A focus on the nature of strategic management process and its contribution to answer fundamental question of how firms achieve sustainable competitive advantage and improved performance through use of suitable leadership has led to organizational leaders formulating strategies through approaches that are systematic, rational as well as logical to strategic choices at corporate, business and functional levels. This has continued to be a major milestone in strategic management process in establishing a clear strategy that makes an organization competitive through suitable management of its workforce and application of strategies that are not easily imitated. This calls for an appropriate leadership strategy aimed at eliminating the gap in human capital that exists in various levels of strategic management process in an organization between the current and desired state of leadership in the future that will address the organization deficiencies by considering the right number of leaders required by the firm for the next at least 5 years with required leadership qualities, skills, behaviour, team capabilities and appropriate culture. This study has focused on a comprehensive review of conceptual and theoretical literature that brings out the role of leadership strategy in the context of strategic management process that leads to improved organizational performance. The concept of organizational capabilities and organization context were found to play a relevant role that mediate and moderate this relationship respectively. The study has proposed a suitable theoretical framework that links leadership strategy, organizational capabilities, and context and organization performance based on the identified gaps for guiding future research on leadership strategy. Six theories including; Pasmore model as lead theory, Path-Goal theory, Contingency theory, transformational leadership, Resource based view and Upper echelon theory underpinned the study in understanding the constructs. In summary, based on the reviewed literature, the study on leadership strategy is found critical for organization success and this calls for further testable empirical data and analysis to validate this claim.

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.References

The beginning of the 21st century, the phenomenon of globalization, the IT revolution and the financialization of the economy have also changed the terms of the comparison among capitalist countries. At global level, the rapid expansion of the financial sector was also encouraged by an increase in innovative financial products. Regulators and supervisors have not been able to adequately identify and address the growing risks in the financial system. The beginning of the financial crisis has brought to light such weaknesses. And it is from this negative experience that the major world authorities have intervened, trying to set up plans and regulations to protect the financial system and consumers. The analysis of the framework that comes with the financial crisis of 2007-2013 is thus a starting point for this work to understand the new features of world capitalism. American and European capitalist systems seem to diverge above all on the policies and instruments for regulating the financial system. The aim of the work is to show the differences between the US and European financial and banking regulation. The former is geared towards reviving deregulation and financial innovation while the latter is more geared towards redesigning a more accentuated regulatory model with a governance of the economy that always sees the presence of a mixed welfare and market system.

This study plans to investigate the influence of intellectual capital on bank profitability and value and bank profitability on bank market value. Furthermore, 34 banks listed on Indonesia's capital market are taken from the population by the simple random sampling technique. Path analysis model acting as the method to evaluate the variables-related data. From the hypothesis testing, this study affirms a positive impact of intellectual capital on bank profitability and value and the similar effect of bank profitability on bank value. The implications related to the application of intellectual capital in banks are attached.

In recent years, the combined effects of inflation, recession, high-interest rates, new investment media and technological advances in information processing have made “Cash Management” an increasingly important and complex subject (IMF, 2001; Ter-Minassian and Parente, 1995). At times, the desire and effort to squeeze out the most from every dollar and to minimise idle cash balances has become an obsession with many organisations and countries. Therefore, effective and efficient Cash Management has become a “sine qua non” for the success of any business organisation (Horcher, 2006a; White, 2006). And Countries, “as Corporate Entities”, are no exceptions to this basic fundamental business principle (Wood & Sangster, 2012). The Theoretical Framework of this study was underpinned by the Stakeholder Theory (Freeman, 1984), the Financial Management Theory (Hayes and Nolan, 1974; Kingston, 1973), and the Modern Money Theory (Friedman, 1964; Keynes, 1930; Mitchell-Innes, 1914). We conducted a cross-sectional research through non-probability and purposive sampling with 200 respondents. Our face-to-face interviews, structured closed-ended and open-ended Questionnaires which were administered online through email application via Google Forms (as a result of the novel, dreaded, and disruptive Covid-19 pandemic), coupled with PETS (Khan and Pessoa, 2010; Reinikka and Svennson, 2006) resulted in startling revelations. Our major finding was that a government lacking an efficient and effective control over its cash resources will definitely pay for its institutional deficiencies in multiple ways (Ahmed, 2016).

Differences in socio-economic conditions, demographics, and infrastructural variances in the rural markets create substantial variances in the consumption patterns compared with the urban markets. As per a report of the World Bank, about 63 percent of the population resides in Bangladesh’s rural areas as it constitutes a bigger market for the consumption of different products. The researchers envision identifying the impact of packaging and labeling elements in the decision-making evaluation for different skincare products. The research exploration comprises of four objectives, i.e., perception and attitude towards packaging and labeling elements for the rural consumers, measuring the relative impact based on the demographical changes, determining the sensitivity towards branding elements and communication portrayed, and the ability in identifying the original skincare products in comparison with the copy products in the rural markets. The researchers conducted surveys in the Mymensingh, Netrokona, Jamalpur, and Sherpur Districts of the greater Mymensingh Division of Bangladesh. A total of 390 responses were selected based on the non-probability sampling frame. The target sample age group was chosen between 13 to 54 years. Researchers developed a questionnaire in combination with close-ended questions along with simple dichotomies. For analyzing the outcomes, the researchers have applied a statistical method of the “Z-test” Technique. The researchers identified that the packaging and labeling of skincare products influence rural consumers’ behavior. Different promotional strategies and trade marketing activities are recommended based on the findings, reinforcing the rural consumers’ acceptance of skincare products.

Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale, interdisciplinary meta-analysis, to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported from 1992 to 2017. We examine 607 empirical estimates of loss aversion from 150 articles in economics, psychology, neuroscience, and several other disciplines. Our analysis indicates that the mean loss aversion coefficient is 1.955 with a 95% probability that the true value falls in the interval [1.820, 2.105]. We record several observable characteristics of the study designs. Few characteristics are substantially correlated with differences in the mean estimates.